Near-infrared (NIR) spectroscopy is a nondestructive method that provides simple, fast multiconstituent analysis on virtually any fluid and, although NIR spectroscopy is a secondary analytical method because the calibration and validation of measured NIR spectral data is correlated through statistical methods to reference data, it provides levels of accuracy and precision that are nearly comparable to primary reference methods. NIR test samples require no preparation or pretreatment with hazardous chemicals, solvents, or reagents, and the resulting NIR spectra contain a wealth of chemical and physical information on the sample and its constituents.
The processing of NIR absorption spectra, however, is often quite complex because the spectra often include broad overlapping NIR absorption bands that require special mathematical procedures for data analysis. In practice, NIR identification is performed by comparing a sample spectrum to a reference spectra of known materials, and mathematical models and so-called multivariate data analysis, or chemometrics, are used for NIR quantification.
In a laboratory, it may be practical to create new calibration models for each analyte tested or to have a library of models that can be accessed and substituted as necessary during testing depending on the process conditions for the specific sample being tested. In many practical applications, however, testing occurs in an environment in which the process conditions are fluctuating greatly. This is particularly true in instances in which measurements are being taken in real-time and the operator has no ability to standardize conditions, separate samples, or otherwise control the conditions under which the samples being tested are presented.
For example, the changing oil and gas market has increased the need for accurate, reliable hydrocarbon analysis. At the same time, the increase in the use of road and rail to transport crude and concentrate has strained the existing terminal loading infra-structure, resulting in the need to quickly analyze products delivered from different trucks or rail cards, each of which may be delivering hydrocarbon fluids under vastly different conditions. NIR spectroscopy is a very useful tool for measuring the properties of these fluids in-line, at pressure, with no sampling required. However, because the test conditions change frequently, the reference analytical models used in the NIR spectroscopy process are necessarily broad. As a result, the accuracy of the results obtained from such testing is reduced.
There is a need, therefore, for a method and system configured to easily test the composition of fluids using NIR spectroscopy under circumstances in which the process conditions are fluctuating.